RAPID: Socio-technical systems and Big Data Analytics in the Ebola Response

RAPID：埃博拉应对中的社会技术系统和大数据分析

• 批准号: 1519023
• 负责人: Carleen Maitland
• 金额: $ 9.99万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-12-15 至 2016-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1519023&HistoricalAwards=false
• 关键词: RAPID; Socio; technical; systems; Big

In response to the 2014 Ebola virus outbreak in western Africa, dozens of humanitarian relief organizations as well as the CDC and U.S. military are providing medical assistance or logistics support to the relief effort. At the same time, a diverse range of volunteer technical communities (VTCs) and academics, as well as the humanitarian relief organizations themselves, are attempting to make use of "big data" to improve the response. These big data analyses are based on diverse data from diverse sources, including call records from mobile phone companies, health worker inventories from ministries, and daily case-data reports aggregated from multiple organizations. The analyses have generated outputs such as visually appealing maps and predictions of outbreak trajectories. However, precisely how, when and where these analyses can be used effectively by response organizations are still open questions. The project will develop knowledge to guide response organizations interested in leveraging existing and emerging big data from a variety of sources (response organizations, firms, government, individuals), which in turn may improve the speed, quality, and efficiency of crisis response. The research team of computer and social scientists will partner with a consultant with expertise in crisis information management deployed in the Ebola response. They will examine both organizational and technical dimensions of the use of big data analytics in the Ebola response organizations, carrying out a series of interviews to investigate how and where data is used (field, headquarters, or both) and the work involved to make big data analyses usable in the decision making of response organizations. The results will inform the development of a socio-technical systems framework to explain what makes big data analyses useable. The social dimensions of the framework will include the response context as well as decision making processes. The technical dimensions will include data availability, data analyses and output formats. In the process of developing this socio-technical framework, this research will identify mechanisms for matching organizational needs with big data analyses. More importantly, however, by identifying these mechanisms, the research will shed light on the fundamental roles of multi-level governance and articulation work in making effective use of big data analyses. The multi-level approach helps explain and predict the location in an organization?s hierarchy of technical and decision making expertise. Within these levels, a focus on articulation work helps specify the necessary tasks for using data in a highly dynamic environment. The project will extend the scholarship in the crisis informatics sub-field of big data beyond its current focus on social media data and provide clarification of "last mile" issues in big data aimed at ensuring the usefulness of output.

为了应对2014年西非爆发的埃博拉病毒，数十个人道主义救援组织以及疾控中心和美国军方正在为救援工作提供医疗援助或后勤支持。与此同时，各式各样的志愿技术团体(VTC)和学者以及人道主义救援组织本身都在试图利用“大数据”来改进应对措施。这些大数据分析基于来自不同来源的各种数据，包括移动电话公司的通话记录、各部委的卫生工作者库存，以及来自多个组织的每日病例数据报告。这些分析产生了诸如视觉上吸引人的地图和暴发轨迹预测等输出。然而，响应组织究竟如何、何时以及在哪里有效地使用这些分析仍然是悬而未决的问题。该项目将开发知识来指导有兴趣利用来自各种来源(响应组织、公司、政府、个人)的现有和新兴大数据的响应组织，这反过来可能会提高危机响应的速度、质量和效率。由计算机和社会科学家组成的研究团队将与一名在应对埃博拉病毒时部署的具有危机信息管理专业知识的顾问合作。他们将审查在埃博拉应对组织中使用大数据分析的组织和技术层面，进行一系列访谈，以调查数据的使用方式和地点(外地、总部或两者)，以及为使大数据分析可用于应对组织的决策所涉及的工作。这些结果将为社会技术系统框架的发展提供信息，以解释是什么使大数据分析变得有用。该框架的社会层面将包括应对背景和决策过程。技术层面将包括数据可获得性、数据分析和输出格式。在制定这一社会技术框架的过程中，这项研究将确定将组织需求与大数据分析相匹配的机制。然而，更重要的是，通过确定这些机制，这项研究将阐明多层次治理和衔接工作在有效利用大数据分析方面的基础作用。多层次方法有助于解释和预测组织中的位置-S的技术和决策专业知识层次。在这些级别中，对衔接工作的关注有助于指定在高度动态的环境中使用数据的必要任务。该项目将扩大大数据危机信息学分支领域的学术研究范围，使其超越目前对社交媒体数据的关注，并澄清大数据中的“最后一英里”问题，旨在确保产出的有用性。